AFTE Store - Estimates of Striation Pattern Identification Error Rates by Algorithmic Methods

This study presents a computationally based methodology to estimate identification error rates of striation patterns in as modern and objective way as possible. A database was assembled consisting of 3D striation patterns generated by standard tip screwdrivers and 9-mm Glock firing pin apertures. These toolmark surfaces were digitally recorded by white light confocal microscopy commonly used for surface metrology applications. Multivariate algorithmic methods were used which encompass few assumptions and have a long and successful application history in many scientific fields. Specifically, principal component analysis and support vector machine methodology were exploited to objectively associate striation patterns with the tools that created them. Estimated toolmark identification error rates were far less than 1% so long as enough toolmark data is used to train the algorithm. Realizing that our approach to this problem is not the only one possible and to stimulate interest in constructing an open reference database of toolmarks and computer programs, all of the data and software generated for this study is available at http://toolmarkstatistics.jjay.cuny.edu/ to registered users for free.